New Perspectives on Game-Based Assessment with Process Data and Physiological Signals

  • Steve Nebel
  • Manuel NinausEmail author
Part of the Advances in Game-Based Learning book series (AGBL)


The unfolding empowerment of instructors as game designers with approachable and widely available tools such as Scratch, Minecraft, or Unreal Engine shifted the perspective on game-based assessment (GBA). An increasing number of instructors are capable of creating games themselves, subsequently gaining access to the mechanics and the embedded data. Thus, detailed information regarding each individual player becomes accessible. This gains importance, as this approach might amplify the availability of desperately needed process data within the fields of instructional psychology and game-based learning research. However, this approach is still in its infancy, and future users and researchers need guidance regarding the gathering and the interpretation of insights created by complex process data within GBA. This becomes particularly important with the increasing use of physiological data in learning as well as assessment scenarios. The ubiquitous availability of sensors acquiring physiological data allows for new and noninvasive ways of acquiring objective real-time data that can provide deeper insights into emotional and cognitive states of players. As the technology becomes less expensive and increasingly novice-friendly, more applications are emerging, ranging from GBA within sport applications to scientific experiments that attempt to connect physiological measures to psychological concepts. This chapter highlights the potentials of process, as well as physiological data, but also the problems that can arise in this context. Finally, this chapter aims to provide a new perspective on the emerging trend of using such data within GBA.


Process data Physiological data Data analysis Educational video games Instructional psychology Adaptivity 



The current research was funded by the Leibniz-Competition Fund (SAW-2016-IWM-3) and the Leibniz-WissenschaftsCampus “Cognitive Interfaces” (MWK-WCT TP12) supporting Manuel Ninaus.


  1. Admiraal, W., Huizenga, J., Akkerman, S., & Ten Dam, G. (2011). The concept of flow in collaborative game-based learning. Computers in Human Behavior, 27(3), 1185–1194. Scholar
  2. Alexander, P. A. (2018). Past as prologue: Educational psychology’s legacy and progeny. Journal of Educational Psychology, 110(2), 147–162. Scholar
  3. Allison, B. Z., & Polich, J. (2008). Workload assessment of computer gaming using a single-stimulus event-related potential paradigm. Biological Psychology, 77(3), 277–283. Scholar
  4. Aviezer, H., Trope, Y., & Todorov, A. (2012). Body cues, not facial expressions, discriminate between intense positive and negative emotions. Science, 338(6111), 1225–1229. Scholar
  5. Azevedo, R., Harley, J., Trevors, G., Duffy, M., Feyzi-Behnagh, R., Bouchet, F., & Landis, R. (2013). Using trace data to examine the complex roles of cognitive, metacognitive, and emotional self-regulatory processes during learning with multi-agent systems. In R. Azevedo & V. Aleven (Eds.), International handbook of metacognition and learning technologies (Vol. 28, pp. 427–449). New York, NY: Springer. Scholar
  6. Baumgartner, T., Speck, D., Wettstein, D., Masnari, O., Beeli, G., & Jäncke, L. (2008). Feeling present in arousing virtual reality worlds: Prefrontal brain regions differentially orchestrate presence experience in adults and children. Frontiers in Human Neuroscience, 2, 1–12. Scholar
  7. Baumgartner, T., Valko, L., Esslen, M., & Jäncke, L. (2006). Neural correlate of spatial presence in an arousing and noninteractive virtual reality: An EEG and psychophysiology study. Cyberpsychology & Behavior, 9(1), 30–45. Scholar
  8. Berta, R., Bellotti, F., De Gloria, A., Pranantha, D., & Schatten, C. (2013). Electroencephalogram and physiological signal analysis for assessing flow in games. IEEE Transactions on Computational Intelligence and AI in Games, 5(2), 164–175. Scholar
  9. Bower, M., & Sturman, D. (2015). What are the educational affordances of wearable technologies? Computers & Education, 88, 343–353. Scholar
  10. Box, G. E. P., & Tiao, G. C. (2011). Bayesian inference in statistical analysis. New York, NY: Wiley.Google Scholar
  11. Boyle, E. A., Hainey, T., Connolly, T. M., Gray, G., Earp, J., Ott, M., … Pereira, J. (2016). An update to the systematic literature review of empirical evidence of the impacts and outcomes of computer games and serious games. Computers & Education, 94, 178–192. Scholar
  12. Calvo-Morata, A., Alonso-Fernández, C., Freire, M., Martínez-Ortiz, I., & Fernández-Manjón, B. (2018). Making understandable game learning analytics for teachers. In G. Hancke, M. Spaniol, K. Osathanunkul, S. Unankard, & R. Klamma (Eds.), Advances in Web-Based Learning – ICWL 2018 (pp. 112–121). Cham, Switzerland: Springer. Scholar
  13. Charles, S. T., Reynolds, C. A., & Gatz, M. (2001). Age-related differences and change in positive and negative affect over 23 years. Journal of Personality and Social Psychology, 80(1), 136–151. Scholar
  14. Charles University, Czech Academy of Sciences. (2017). Attentat 1942 [Computer Software]. Prague, Czechoslovakia: Author.Google Scholar
  15. Cohn, J. F., Ambadar, Z., & Ekman, P. (2007). Observer-based measurement of facial expression with the Facial Action Coding System. In J. A. Coan & J. J. B. Allen (Eds.), Handbook of emotion elicitation and assessment. Series in affective science (pp. 203–221). New York, NY: Oxford University Press.Google Scholar
  16. Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. New York, NY: Harper Perennial.Google Scholar
  17. Dasgupta, S., Clements, S. M., Idlbi, A. Y., Willis-Ford, C., & Resnick, M. (2015). Extending Scratch: New pathways into programming. In 2015 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) (pp. 165–169). Atlanta, GA: IEEE. Scholar
  18. Drachsler, H., & Greller, W. (2016). Privacy and analytics: It’s a delicate issue a checklist for trusted learning analytics. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge - LAK ’16 (pp. 89–98). New York, NY: ACM. Scholar
  19. Epic Games. (2017). Fortnite [Computer Software]. Cary, NC: Author.Google Scholar
  20. Epic Games. (2018). Unreal Engine (Version 4) [Computer Software]. Cary, NC: Author.Google Scholar
  21. Eysink, T. H., de Jong, T., Berthold, K., Kolloffel, B., Opfermann, M., & Wouters, P. (2009). Learner performance in multimedia learning arrangements: An analysis across instructional approaches. American Educational Research Journal, 46(4), 1107–1149. Scholar
  22. Ferrari, M., & Quaresima, V. (2012). A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application. NeuroImage, 63(2), 921–935. Scholar
  23. Freeman, J., Avons, S. E., Pearson, D. E., & IJsselsteijn, W. A. (1999). Effects of sensory information and prior experience on direct subjective ratings of presence. Presence: Teleoperators & Virtual Environments, 8(1), 1–13. Scholar
  24. GameAnalytics. (2016). GameAnalytics [Computer Software]. Copenhagen, Denmark: Author.Google Scholar
  25. Girouard, A., Solovey, E. T., Hirshfield, L. M., Chauncey, K., Jacob, R. J. K., Sassaroli, A., … Jacob, R. J. K. (2009). Distinguishing difficulty levels with non-invasive brain activity measurements. In T. Gross, J. Gulliksen, P. Kotzé, L. Oestreicher, & P. Palanque (Eds.), Human-Computer Interaction – INTERACT 2009 (pp. 440–452). Heidelberg, Germany: Springer. Scholar
  26. GitHub. (2018). GitHub [Computer Software]. San Francisco, CA: Author.Google Scholar
  27. Granic, I., Lobel, A., & Engels, R. C. (2014). The benefits of playing video games. American Psychologist, 69(1), 66–78. Scholar
  28. Greipl, S., Ninaus, M., Bauer, D., Kiili, K., & Moeller, K. (2018). A fun-accuracy trade-off in game-based learning. In M. Gentile, M. Allegra, & H. Söbke (Eds.), International Conference on Games and Learning Alliance – Lecture Notes in Computer Science (pp. 167–177). Cham, Switzerland: Springer. Scholar
  29. Guo, G., & Dyer, C. R. (2005). Learning from examples in the small sample case: Face expression recognition. IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics, 35(3), 477–488. Scholar
  30. Hamari, J., Shernoff, D. J., Rowe, E., Coller, B., Asbell-Clarke, J., & Edwards, T. (2016). Challenging games help students learn: An empirical study on engagement, flow and immersion in game-based learning. Computers in Human Behavior, 54, 170–179. Scholar
  31. Hattahara, S., Fujii, N., Nagae, S., Kazai, K., & Katayose, H. (2008). Brain activity during playing video game correlates with player level. In Proceedings of the 2008 International Conference on Advances in Computer Entertainment Technology (pp. 360–363). New York, NY: ACM. Scholar
  32. Howard-Jones, P., & Jay, T. (2016). Reward, learning and games. Current Opinion in Behavioral Sciences, 10, 65–72. Scholar
  33. Kalyuga, S., & Singh, A. M. (2016). Rethinking the boundaries of cognitive load theory in complex learning. Educational Psychology Review, 28(4), 831–852. Scholar
  34. Kang, J., Liu, M., & Qu, W. (2017). Using gameplay data to examine learning behavior patterns in a serious game. Computers in Human Behavior, 72, 757–770. Scholar
  35. Kiili, K., Lindstedt, A., & Ninaus, M. (2018). Exploring characteristics of students’ emotions, flow and motivation in a math game competition. In J. Koivisto & J. Hamari (Eds.), Proceedings of the 2nd International GamiFIN Conference (pp. 20–29). Pori, Finland: CEUR Workshop Proceedings.Google Scholar
  36. Kivikangas, J. M. (2006). Psychophysiology of flow experience: An explorative study (Master’s thesis). Retrieved from
  37. Klasen, M., Weber, R., Kircher, T. T. J., Mathiak, K. A., & Mathiak, K. (2012). Neural contributions to flow experience during video game playing. Social Cognitive and Affective Neuroscience, 7(4), 485–495. Scholar
  38. Klinkenberg, S., Straatemeier, M., & van der Maas, H. L. (2011). Computer adaptive practice of maths ability using a new item response model for on the fly ability and difficulty estimation. Computers & Education, 57(2), 1813–1824. Scholar
  39. Kober, S. E., & Neuper, C. (2012). Using auditory event-related EEG potentials to assess presence in virtual reality. International Journal of Human-Computer Studies, 70(9), 577–587. Scholar
  40. Lamnek, S., & Krell, C. (2016) Qualitative Sozialforschung [Qualitative social research]. Weinheim, Germany: Beltz Verlagsgruppe.Google Scholar
  41. Littlewort, G. C., Bartlett, M. S., Salamanca, L. P., & Reilly, J. (2011). Automated measurement of children’s facial expressions during problem solving tasks. Face and Gesture, 2011, 30–35. Scholar
  42. Liu, Y., Kosmadoudi, Z., Sung, R. C. W., Lim, T., Louchart, S., & Ritchie, J. (2010). Capture user emotions during computer- aided design. In Proceedings of the Integrated Design and Manufacturing in Mechanical Engineering (IDMME) and Virtual Conference (pp. 2–4).Google Scholar
  43. Lomas, J. D., Koedinger, K., Patel, N., Shodhan, S., Poonwala, N., & Forlizzi, J. L. (2017). Is difficulty overrated? The effects of choice, novelty and suspense on intrinsic motivation in educational games. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (pp. 1028–1039). New York, NY: ACM. Scholar
  44. Maloney, J., Peppler, K., Kafai, Y. B., Resnick, M., & Rusk, N. (2008). Programming by choice: Urban youth learning programming with scratch. In Proceedings of the 39th SIGCSE Technical Symposium on Computer Science Education (pp. 367–371). Portland, OR: ACM. Scholar
  45. Maloney, J., Resnick, M., Rusk, N., Silverman, B., & Eastmond, E. (2010). The scratch programming language and environment. ACM Transactions on Computing Education, 10(4), 1–15. Scholar
  46. Mandryk, R. L., & Atkins, M. S. (2007). A fuzzy physiological approach for continuously modeling emotion during interaction with play technologies. International Journal of Human-Computer Studies, 65(4), 329–347. Scholar
  47. Mandryk, R. L., Atkins, M. S., & Inkpen, K. M. (2006). A continuous and objective evaluation of emotional experience with interactive play environments. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems CHI 06 (pp. 1027–1036). New York, NY: ACM. Scholar
  48. Marklund, B. B., Backlund, P., & Johannesson, M. (2013). Children’s collaboration in emergent game environments. In Proceedings of the 8th International Conference on the Foundations of Digital Games (pp. 306–313). New York, NY: ACM.Google Scholar
  49. Massachusetts Institute of Technology. (2012). MIT App Inventor [Computer Software]. Cambridge, MA: Author.Google Scholar
  50. Mayer, R. E. (2014). Computer games for learning: An evidence-based approach. Cambridge, MA: MIT.CrossRefGoogle Scholar
  51. Mayer, R. E. (2015). On the need for research evidence to guide the design of computer games for learning. Educational Psychologist, 50(4), 349–353. Scholar
  52. Mayer, R. E. (2018). Educational psychology’s past and future contributions to the science of learning, science of instruction, and science of assessment. Journal of Educational Psychology, 110(2), 174–179. Scholar
  53. Microsoft. (2009). Kodu [Computer Software]. Redmond, WA: Author.Google Scholar
  54. Mionix. (2018). Naos QG [Apparatus and Software]. Växjö, Sweden: Author.Google Scholar
  55. Mojang. (2018). MinecraftEdu [Computer Software]. Stockholm, Sweden: Author.Google Scholar
  56. Nacke, L., & Lindley, C. A. (2008). Flow and immersion in first-person shooters. In Proceedings of the 2008 Conference on Future Play Research, Play, Share - Future Play ’08 (pp. 81–88). New York, NY: ACM. Scholar
  57. Nacke, L. E., Grimshaw, M. N., & Lindley, C. A. (2010). More than a feeling: Measurement of sonic user experience and psychophysiology in a first-person shooter game. Interacting with Computers, 22(5), 336–343. Scholar
  58. Nebel, S. (2017). Investigating the mechanisms of competition within educational video games - Die Mechanismen des Wettbewerbs in digitalen Lernspielen (Doctoral dissertation).
  59. Nebel, S., Beege, M., Schneider, S., & Rey, G. D. (2016). The higher the score, the higher the learning outcome? Heterogeneous impacts of leaderboards and choice within educational videogames. Computers in Human Behavior, 65, 391–401. Scholar
  60. Nebel, S., Schneider, S., Beege, M., & Rey, G. D. (2017). Leaderboards within educational videogames: The impact of difficulty, effort and gameplay. Computers & Education, 113, 28–41.CrossRefGoogle Scholar
  61. Nebel, S., Schneider, S., & Rey, G. D. (2016). Mining learning and crafting scientific experiments: A literature review on the use of Minecraft in education and research. Journal of Educational Technology & Society, 19(2), 355–366.Google Scholar
  62. Nebel, S., Schneider, S., Schledjewski, J., & Rey, G. D. (2017). Goal-setting in educational video games: Comparing goal-setting theory and the goal-free effect. Simulation & Gaming, 48(1), 98–130. Scholar
  63. Ninaus, M., Kober, S. E., Friedrich, E. V. C., Dunwell, I., De Freitas, S., Arnab, S., … Neuper, C. (2014). Neurophysiological methods for monitoring brain activity in serious games and virtual environments: A review. International Journal of Technology Enhanced Learning, 6(1), 78–103. Scholar
  64. Ninaus, M., Kober, S. E., Friedrich, E. V. C., Neuper, C., & Wood, G. (2014). The potential use of neurophysiological signals for learning analytics. In 2014 6th International Conference on Games and Virtual Worlds for Serious Applications (VS-GAMES) (pp. 1–5). Valletta, Malta: IEEE. Scholar
  65. Ninaus, M., Moeller, K., McMullen, J., & Kiili, K. (2017). Acceptance of game-based learning and intrinsic motivation as predictors for learning success and flow experience. International Journal of Serious Games, 4(3), 15–30. Scholar
  66. Ninja Theory. (2017). Hellblade: Senua’s Sacrifice [Computer Software]. Cambridge, UK: Author.Google Scholar
  67. Nourbakhsh, N., Chen, F., Wang, Y., & Calvo, R. A. (2017). Detecting users’ cognitive load by galvanic skin response with affective interference. ACM Transactions on Interactive Intelligent Systems, 7(3), 1–20. Scholar
  68. Novak, E., & Johnson, T. E. (2012). Assessment of student’s emotions in game-based learning. In D. Ifenthaler, D. Eseryel, & X. Ge (Eds.), Assessment in game-based learning (pp. 379–399). New York, NY: Springer. Scholar
  69. Nyamsuren, E., Van der Vegt, W., & Westera, W. (2017). Automated adaptation and assessment in serious games: A portable tool for supporting learning. In M. Winands, H. van den Herik, & W. Kosters (Eds.), Advances in computer games (pp. 201–212). Cham, Switzerland: Springer.CrossRefGoogle Scholar
  70. Paas, F., & Sweller, J. (2014). Implications of cognitive load theory for multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 27–42). Cambridge, MA: Cambridge University Press.CrossRefGoogle Scholar
  71. Peifer, C. (2012). Psychophysiological correlates of flow-experience. In S. Engeser (Ed.), Advances in flow research (pp. 139–164). New York, NY: Springer. Scholar
  72. Pekrun, R. (2011). Emotions as drivers of learning and cognitive development. In R. A. Calvo & S. K. D’Mello (Eds.), New perspectives on affect and learning technologies (pp. 23–39). New York, NY: Springer. Scholar
  73. Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., & Perry, R. P. (2011). Measuring emotions in students’ learning and performance: The Achievement Emotions Questionnaire (AEQ). Contemporary Educational Psychology, 36(1), 36–48. Scholar
  74. Pellouchoud, E., Smith, M. E., McEvoy, L., & Gevins, A. (1999). Mental effort-related EEG modulation during video-game play: Comparison between juvenile subjects with epilepsy and normal control subjects. Epilepsia, 40(s4), 38–43. Scholar
  75. Perez-Colado, I., Alonso-Fernandez, C., Freire, M., Martinez-Ortiz, I., & Fernandez-Manjon, B. (2018). Game learning analytics is not informagic! In 2018 IEEE Global Engineering Education Conference (EDUCON) (pp. 1729–1737). Tenerife, Spain: IEEE.
  76. Perttula, A., Kiili, K., Lindstedt, A., & Tuomi, P. (2017). Flow experience in game based learning – A systematic literature review. International Journal of Serious Games, 4(1).
  77. Plass, J. L., & Kaplan, U. (2016). Emotional design in digital media for learning. In S. Tettegah & M. Gartmeier (Eds.), Emotions, technology, design, and learning (pp. 131–161). New York, NY: Elsevier. Scholar
  78. Pugnetti, L., Mendozzi, L., Barbieri, E., Rose, F. D., Attree, E. A., & Barberi, E. (1996). Nervous system correlates of virtual reality experience. In P. M. Sharkey (Ed.), Proceedings of the First European Conference on Disability, Virtual Reality and Associated Technology (pp. 239–246). Maidenhead, UK: The University of Reading.Google Scholar
  79. Ratcliffe, D. (2017). ComputercraftEdu [Computer software]. Cambridge, UK: Author.Google Scholar
  80. Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods. Thousand Oaks, CA: Sage.Google Scholar
  81. Rey, G. D., & Wender, K. F. (2011). Neuronale Netze: eine Einführung in die Grundlagen, Anwendungen und Datenauswertung. Bern, Switzerland: Huber.Google Scholar
  82. Salminen, M., & Ravaja, N. (2007). Oscillatory brain responses evoked by video game events: The case of super monkey ball 2. Cyberpsychology & Behavior, 10(3), 330–338. Scholar
  83. Salminen, M., & Ravaja, N. (2008). Increased oscillatory theta activation evoked by violent digital game events. Neuroscience Letters, 435(1), 69–72. Scholar
  84. Schneider, J., Börner, D., Van Rosmalen, P., & Specht, M. (2015). Augmenting the senses: A review on sensor-based learning support. Sensors, 15(2), 4097–4133. Scholar
  85. Schneider, S., Nebel, S., & Rey, G. D. (2016). Decorative pictures and emotional design in multimedia learning. Learning and Instruction, 44, 65–73. Scholar
  86. Selvaraj, J., Murugappan, M., Wan, K., & Yaacob, S. (2013). Classification of emotional states from electrocardiogram signals: A non-linear approach based on hurst. Biomedical Engineering Online, 12(1), 44. Scholar
  87. Shute, V., & Wang, L. (2016). Assessing and supporting hard-to-measure constructs in video games. In A. A. Rupp & J. P. Leighton (Eds.), The Wiley handbook of cognition and assessment (pp. 535–562). Hoboken, NJ: Wiley.CrossRefGoogle Scholar
  88. Shute, V. J., & Ventura, M. (2013). Stealth assessment: Measuring and supporting learning in video games. Cambridge, MA: MIT.CrossRefGoogle Scholar
  89. Smith, S. P., Blackmore, K., & Nesbitt, K. (2015). A meta-analysis of data collection in serious games research. In C. Loh, Y. Sheng, & D. Ifenthaler (Eds.), Serious games analytics (pp. 31–55). Cham, Switzerland: Springer. Scholar
  90. Solovey, E., Schermerhorn, P., Scheutz, M., Sassaroli, A., Fantini, S., & Jacob, R. (2012). Brainput: Enhancing interactive systems with streaming fnirs brain input. In Proceedings of the 2012 ACM Annual Conference on Human Factors in Computing Systems - CHI ’12 (pp. 2193–2202). New York, NY: ACM. Scholar
  91. Strait, M., Canning, C., & Scheutz, M. (2013). Limitations of NIRS-based BCI for realistic applications in human-computer interaction. In Proceedings of the Fifth International Brain-Computer Interface Meeting (pp. 2–3). Graz, Austria: Graz University of Technology Publishing House. Scholar
  92. Sweller, J. (1994). Cognitive load theory, learning difficulty and instructional design. Learning and Instruction, 4(4), 295–312. Scholar
  93. Taub, M., Mudrick, N. V., Azevedo, R., Millar, G. C., Rowe, J., & Lester, J. (2017). Using multi-channel data with multi-level modeling to assess in-game performance during gameplay with Crystal Island. Computers in Human Behavior, 76, 641–655. Scholar
  94. Thauros-Clan. (2016). Brain computer interface plugin [Computer Software]. Author.Google Scholar
  95. Valve Corporation. (2012). Counter Strike: Global Offensive [Computer Software]. Bellevue, WA: Author.Google Scholar
  96. Vorderer, P., Wirth, W., Gouveia, F. R., Biocca, F., Saari, T., Jäncke, F., … Jäncke, P. (2004). MEC Spatial Presence Questionnaire (MECSPQ): Short documentation and instructions for application. Report to the European Community, Project Presence: MEC (IST-2001-37661). Retrieved from
  97. Vorderer, P., Wirth, W., Saari, T., Gouveia, F. R., Biocca, F., Jäncke, F., … Jäncke, P. (2003). Constructing presence: Towards a two-level model of the formation of Spatial Presence. Unpublished report to the European Community, Project Presence: MEC (IST-2001-37661). Hannover, Munich, Helsinki, Porto, Zurich.Google Scholar
  98. Wirzberger, M., Herms, R., Esmaeili Bijarsari, S., Rey, G. D., & Eibl, M. (2017). Influences of cognitive load on learning performance, speech and physiological parameters in a dual-task setting. In Poster session presented at the meeting of the 20th Conference of the European Society for Cognitive Psychology, Potsdam, Germany.Google Scholar
  99. Wise, R. A. (2004). Dopamine, learning and motivation. Nature Reviews Neuroscience, 5(6), 483–494. Scholar
  100. Witte, M., Ninaus, M., Kober, S. E. S. E., Neuper, C., & Wood, G. (2015). Neuronal correlates of cognitive control during gaming revealed by near-infrared spectroscopy. PLoS One, 10(8), e0134816. Scholar
  101. Wu, C. H., Huang, Y. M., & Hwang, J. P. (2016). Review of affective computing in education/learning: Trends and challenges. British Journal of Educational Technology, 47(6), 1304–1323. Scholar
  102. Xiao, X., & Wang, J. (2016). Context and cognitive state triggered interventions for mobile MOOC learning. In ICMI ’16: Proceedings of the 18th ACM International Conference on Multimodal Interaction (pp. 378–385). New York, NY: ACM. Scholar
  103. Xue, S., Wu, M., Kolen, J., Aghdaie, N., & Zaman, K. A. (2017). Dynamic difficulty adjustment for maximized engagement in digital games. In WWW ’17 Companion: Proceedings of the 26th International Conference on World Wide Web Companion (pp. 465–471). Geneva, Switzerland: International World Wide Web Conferences Steering Committee and Republic and Canton of Geneva. Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Technische Universität ChemnitzChemnitzGermany
  2. 2.Leibniz-Institut für WissensmedienTübingenGermany

Personalised recommendations